Plot options for
jndplot( x, arrow = c("relative", "absolute", "none"), achro = FALSE, arrow.labels = TRUE, arrow.col = "darkgrey", arrow.p = 1, labels.cex = 1, margin = "recommended", square = TRUE, ... )
(required) the output from a
If and how arrows indicating receptor vectors should be drawn.
Logical. Should the achromatic variable be plotted as a
dimension? (only available for dichromats and trichromats, defaults to
Logical. Should labels be plotted for receptor arrows?
color of the arrows and labels.
scaling factor for arrows.
size of the arrow labels.
"recommended", where the function will choose
margin attributes, or a numerical vector of the form
c(bottom, left, top, right) which gives the number of lines of margin to be specified on the
four sides of the plot. (Default varies depending on plot dimensionality).
logical. Should the aspect ratio of the plot be held to 1:1?
Creates a plot, details of the plot depend on the input data.
arrow argument accepts three options:
"relative": With this option, arrows will be made relative to the data.
Arrows will be centered on the data centroid, and will have an arbitrary
length of half the average pairwise distance between points, which can be
scaled with the
"absolute": With this option, arrows will be made to reflect the visual
system underlying the data. Arrows will be centered on the achromatic point
in colourspace, and will have length equal to the distance to a
monochromatic point (i.e. a colour that stimulates approximately 99.9% of
that receptor alone). Arrows can still be scaled using the
argument, in which case they cannot be interpreted as described.
"none": no arrows will be included.
Pike, T.W. (2012). Preserving perceptual distances in chromaticity diagrams. Behavioral Ecology, 23, 723-728.
# Load floral reflectance spectra data(flowers) # Estimate quantum catches visual phenotype of a Blue Tit vis.flowers <- vismodel(flowers, visual = 'bluetit') # Estimate noise-weighted colour distances between all flowers cd.flowers <- coldist(vis.flowers) #> Quantum catch are relative, distances may not be meaningful #> Calculating noise-weighted Euclidean distances # Convert points to Cartesian coordinates in which Euclidean distances are # noise-weighted. propxyz <- jnd2xyz(cd.flowers) # Plot the floral spectra in 'noise-corrected' space plot(propxyz)